AI Pest Detection System for Agriculture

We are approached by owners of agricultural holdings with a problem: the Colorado potato beetle lays eggs on the underside of the leaf — a place that a drone rarely sees. Thrips are visible only under 10x magnification. Spider mites leave traces that agronomists confuse with sunburn. Pest detection is technically one of the most complex tasks for agricultural computer vision: objects are small, camouflaged, and often hidden by foliage. Our AI pest detection system combines YOLOv8 for pests, AI trap analysis, and drone pest monitoring to provide comprehensive IoT plant protection. We specialize in developing AI systems that solve these problems and are ready to assess your project.

Why Standard CV Approaches Fail Here

The Issue of Object Scale

Aphids on a leaf are 1–2 mm in size. In an image from a drone flying at 10 meters with a GSD of 2 mm/pixel, an aphid occupies literally 1 pixel. This is not detected by any YOLO.

Practical conclusion: for small pests, close-range imaging is essential — automated systems with cameras at plant level (robotized platforms, conveyor systems), camera traps (sticky trap monitoring), or macro shots from a phone app.

For large pests (locusts, Colorado potato beetle, caterpillars) — drones with GSD < 0.5 mm/pixel (flight altitude 3–5 m).

Detector for Small Objects

YOLOv8 and most standard detectors perform poorly on objects smaller than 32×32 pixels. We use several approaches depending on the task:

• Tile-based inference — the image is cut into patches of 640×640 with 20% overlap, each patch is processed separately. SAHI (Sliced Aided Hyper Inference) implements this on top of any YOLO model without changing weights.

• Specialized architectures for small objects — RFLA (Receptive Field Loss for Small Object Detection), QueryDet, or custom FPN with an additional high-resolution output P2.

On a whitefly counting task on sticky traps (yellow glue traps): YOLOv8n with SAHI at tile_size=640 gave mAP50 = 0.79, while standard inference on the full 4000×3000 image gave only 0.52. This is a 52% relative improvement in detection accuracy. Thus, SAHI outperforms standard inference by 1.5 times in mAP50 for small objects.

Pest Counting — A Separate Challenge

Detection of "yes/no" is not enough for making treatment decisions. Quantitative counting per unit area is needed. For dense colonies (aphids, thrips), bounding-box detection shifts to density estimation — CSRNet or DM-Count instead of YOLO, which predict a density map and sum the predicted number of individuals.

Deliverables and What's Included

Each project includes:

• Prototype detection/counting model trained on your data.
• Training and validation on your dataset with detailed accuracy reports.
• Integration with your infrastructure via REST API (documentation provided).
• User guide and best practices for data collection.
• Two months of free support after deployment, including model monitoring and updates.
• Access to our cloud dashboard for visualizing pest counts and alerts.

For a 100-hectare farm, this system can reduce pesticide costs by $2,000–$5,000 per season.

We have 5 years of experience in the agri-AI market and over 15 completed projects. We guarantee counting accuracy of ±15% for trap monitoring under specified imaging conditions.

How to Improve Accuracy on Small Objects?

Trap Monitoring with Automatic Recognition

One of the viable and cost-effective formats: smart pheromone traps with a camera (e.g., Delta Trap + Raspberry Pi Camera v3 or ready-made devices like Trapview). The camera takes a snapshot every 2–4 hours, the model counts insects on the sticky surface, and data is sent to the cloud.

For such a system, MobileNetV3-Small or EfficientNet-Lite0 with INT8 quantization works on Raspberry Pi Zero 2W with under 2W power consumption. Counting accuracy ±15% at densities up to 200 insects per trap.

Development Process (Step by Step)

1. Data collection — The main challenge is the variety of lighting conditions (morning/noon/overcast) and development stages of pests (eggs, larvae, adults look different). A minimum of 300–500 annotated instances of each pest at each stage is required.

2. Annotation — For traps: bounding boxes + counting. For field images: polygon segmentation for precise separation from background. We use Label Studio with a custom insect detection template.

3. Training — Transfer learning from COCO weights (insects are weakly represented there, but low-level features transfer). Focal loss with gamma=1.5 to compensate for background/object imbalance.

4. Production monitoring — Automatic notification when a threshold is exceeded (economic injury level as defined in FAO Guidelines on Pest Management — different for each crop and pest, set by agronomist). Integration with precision agriculture systems.

We also focus on pest outbreak prediction and plant image processing using agricultural MLOps.

Timelines

System for 1–3 pest species on traps: 4–6 weeks. Field multi-species platform with mobile app and API: 2–4 months. Cost is calculated individually; typical investment ranges from $5,000 for a basic trap system to $25,000+ for a full platform. Contact us to assess your project — get a free consultation.